arulesViz - Visualizing Association Rules and Frequent Itemsets with R

This R package extends package arules with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration.

Installation

Stable CRAN version: install from within R with

install.packages("arulesViz")

Current development version: install from GitHub (needs devtools) with

devtools::install_github("mhahsler/arulesViz")

This might also require the development version of arules.

Features

• Visualizations using engines ggplot2 (default engine for most methods), grid, base (R base plots), htmlwidget (powered by plotly and visNetwork).
• Interactive visualizations using grid, plotly and visNetwork.
• Interactive rule inspection with datatable.
• Integrated interactive rule exploration using ruleExplorer.

Available Visualizations:

• Scatterplot, two-key plot
• Matrix and matrix 3D visualization
• Grouped matrix-based visualization
• Several graph-based visualizations
• Doubledecker and mosaic plots
• Parallel Coordinate plot

Usage

Mine some rules.

library("arulesViz")
data("Groceries")
rules <- apriori(Groceries, parameter = list(support = 0.005, confidence = 0.5))

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.5    0.1    1 none FALSE            TRUE       5   0.005      1
##  maxlen target  ext
##      10  rules TRUE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 49 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [120 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [120 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

Standard visualization

plot(rules)

Inspect rules with an interactive datatable

inspectDT(rules)

Interactive visualization with plotly

plot(rules, engine = "plotly")

Interactive visualizations and ruleExplorer

Explore rules or a dataset using the web-based ruleExplorer.

library("shiny")
data("iris")

ruleExplorer(iris)

See resulting ruleExplorer app (hosted on shinyapps.io)

References

• Michael Hahsler and Sudheer Chelluboina. Visualizing Association Rules: Introduction to the R-extension Package arulesViz (with complete examples).
• Michael Hahsler. arulesViz: Interactive visualization of association rules with R. R Journal, 9(2):163-175, December 2017.
• Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977-1981, 2011.